This thesis analyzes the commodity market. First, a machine learning-based model for state prediction of agricultural commodity prices is proposed. Combined with a stochastic price model, state-dependent price scenarios via Monte Carlo simulation are generated. Second, a portfolio optimization problem with stochastic market price of commodity risk is solved. Using stochastic control methods, a stochastic optimal portfolio process is derived.